healthiar overview

healthiar is a R package (= collection of R functions) to quantify and monetize the burden of disease attributable to exposures

DALY = disability-adjusted life years; YLD = years lived with disability; YLL = years of life lost

Refresher - Burden of disease with relative risk

attribute_health() with RR 1/3

Goal: attribute COPD cases to PM2.5 air pollution exposure

results_pm_copd <- attribute_health(
  erf_shape = "log_linear", # erf = exposure-response function
  rr_central = 1.369, 
  rr_increment = 10,  # μg / m^3
  exp_central = 8.85, # μg / m^3
  cutoff_central = 5, # μg / m^3
  bhd_central = 30747 # bhd = baseline health data (here: COPD incidence)
) 

results_pm_copd <- attribute_health(
  erf_shape = "log_linear",
  rr_central = exdat_pm$relative_risk, 
  rr_lower = exdat_pm$relative_risk_lower,
  rr_upper = exdat_pm$relative_risk_upper,
  rr_increment = 10, 
  exp_central = exdat_pm$mean_concentration,
  cutoff_central = exdat_pm$cut_off_value,
  bhd_central = exdat_pm$incidence
) 

attribute_health() with RR 2/3

attribute_health() outputs two lists (“folders”)

• health_main contains the main results

• health_detailed contains more detailed results and other assessment details in sub-lists (“sub-folders”)

results_pm_copd$health_main

attribute_health() with RR 3/3

results_pm_copd <- attribute_health(
  erf_shape = "log_linear",
  rr_central = 1.369, rr_lower = 1.114, rr_upper = 1.664,
  rr_increment = 10,
  exp_central = 8.85, exp_lower = 8, exp_upper = 10,
  cutoff_central = 5,
  bhd_central = 30747, bhd_lower = 28000, bhd_upper = 32000
) 

results_pm_copd$health_detailed$results_raw

attribute_health() with RR and user-defined ERF 1/2

Goal: attribute COPD cases to air pollution exposure by applying a user-defined exposure response function (e.g. MR-BRT curves from Global Burden of Disease study)

• Any function of the form intercept + a x c^1 + b x c^2 + …

• Any other (non-linear) function of the function type, as obtained from e.g. splinefun() or approxfun()

results_pm_copd_mr_brt <- attribute_health(
  exp_central = 8.85,
  bhd_central = 30747,
  erf_eq_central = splinefun(
    x = c(0, 5, 10, 15, 20, 25, 30, 50, 70, 90, 110),
    y = c(1.00, 1.04, 1.08, 1.12, 1.16, 1.20, 1.23, 1.35, 1.45, 1.53, 1.60),
    method = "natural")
)

attribute_health() with RR and user-defined ERF 2/2

Refresher - Burden of disease with absolute risk

attribute_health() with AR 1/2

Goal: attribute cases of high annoyance (HA) to noise exposure

results_noise_ha <- attribute_health(
  approach_risk = "absolute_risk",
  erf_eq_central = "78.9270-3.1162*c+0.0342*c^2",
  exp_central = c(57.5, 62.5, 67.5, 72.5, 77.5),
  pop_exp = c(387500, 286000, 191800, 72200, 7700),
)

attribute_health() with AR 2/2

results_noise_ha$health_main

results_noise_ha$health_detailed$results_raw

attribute_health() across multiple geographic units 1/3

Goal: attribute disease cases to PM2.5 exposure in multiple geographic units, such as municipalities, provinces, countries, …

results_iteration <- attribute_health(
  geo_id_micro = c("Zürich", "Basel", "Geneva", "Ticino", "Jura"), 
  geo_id_macro = c("German","German","French","Italian","French"),
  erf_shape = "log_linear",
  rr_central = 1.369,
  rr_increment = 10, 
  cutoff_central = 5,
  exp_central = c(11, 11, 10, 8, 7),
  bhd_central = c(4000, 2500, 3000, 1500, 500)
)

attribute_health() across multiple geographic units 2/3

results_iteration$health_main

attribute_health() across multiple geographic units 3/3

results_iteration$health_detailed$results_raw

summarize_uncertainty() 1/3

Goal: perform a Monte Carlo simulation to obtain summary uncertainty confidence intervals that combine uncertainty in different input data

results <- 
  attribute_health(
    erf_shape = "log_linear",
    rr_central = 1.369, 
    rr_lower = 1.114, 
    rr_upper = 1.664,
    rr_increment = 10,
    exp_central = 8.85, 
    exp_lower = 8, 
    exp_upper = 10,
    cutoff_central = 5,
    bhd_central = 30747, 
    bhd_lower = 28000, 
    bhd_upper = 32000) 

results <- 
  summarize_uncertainty(
    output_attribute = results,
    n_sim = 100
  )

summarize_uncertainty() 2/3

results$uncertainty_main

summarize_uncertainty() 3/3

results$uncertainty_detailed$attribute_by_sim_disaggregated

compare() 2/2

results_comparison$health_main

Other healthiar functions

• attribute_lifetable() for YLL
• attribute_health() for YLD (using the dw & duration arguments)
• get_daly() as the sum of YLL and YLD
• monetize() monetizes (attributable) health impacts
• cba() performs a cost-benefit analysis (CBA)
• multiexpose() considers exposure to 2 exposures (e.g. two different air pollutants) at the same time to quantify the attributable health impacts
• prepare_exposure() calculates (population-weighted) mean exposure by combining spatial exposure geographic units data
• socialize() determines burden attributable to differences in a social indicator
• attribute_mod() modifies an existing attribute_health() assessment
• prepare_mdi() creates the BEST-COST MDI (Multidimensional Deprivation Index)

Outlook

• healthiar is publicly available on this GitHub page
  • healthiar might be expanded in the future (if funding available)
• Python version coming out later in 2026
• Package submitted to CRAN

monetize() 1/2

Goal: monetize (attributable) health impacts (obtained from healthiar or other source)

• Discounting yes/no
• Inflation yes/no

results_monetization <- monetize(
  output_attribute = results_pm_copd,
  valuation = 20,
  discount_shape = "exponential",
  discount_rate = 0.03,
  discount_years = 5
)

monetize() 2/2

results_monetization$monetization_main

healthiar in RStudio 1/3

Figure: Landing page of the healthiar package in RStudio, where you find the package vignettes and function documentation.

healthiar in RStudio 2/3

Figure: Intro to healthiar vignette (= detailed guide to the package)

healthiar in RStudio 3/3

Figure: Help page of attribute_health()

Export results

write.csv(x = results_pm_copd$health_main, file = "exported_results/results_pm_copd.csv")

save(results_pm_copd, file = "exported_results/results_pm_copd.Rdata")

write.xlsx(x = results_pm_copd$health_main, file = "exported_results/results_pm_copd.xlsx")

Visualize results